Dry matter intake in US Holstein cows: Exploring the genomic and phenotypic impact of milk components and body weight composite.

Dry matter intake (DMI) plays a major role in characterizing feed efficiency in dairy cattle and has been widely used in dairy nutrition research. As feed represents the largest operating cost in dairy production, feed efficiency has gained increased attention for genetic selection. Improved feed efficiency contributes to farm profitability by reducing input costs and to environmental sustainability by reducing greenhouse gas emissions. To estimate feed efficiency, DMI must be accurately determined or estimated, which requires large data sets, and indeed also to estimate feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs and have therefore been estimated in the cited study.

Dry matter intake records from 8 513 lactations of 6 621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included days in milk (DIM), age-parity subclass, trial date, management group, and body weight (BW) change during 28- and 42-day feeding trials in mid lactation.

Phenotypic regressions of DMI on milk (0.014 ± 0.006), fat (3.06 ± 0.01), and protein (4.79 ± 0.25) were much less than corresponding genomic regressions (0.08 ± 0.03, 11.30 ± 0.47, and 9.35 ± 0.87, respectively) or sire genomic regressions multiplied by 2 (0.048 ± 0.04, 6.73 ± 0.94, and 4.98 ± 1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. According to the energy corrected milk (ECM) formula, fat production requires 69% more DMI than protein production. In the phenotypic regression, it was estimated that protein production requires 56% more DMI than fat. However, the genomic regression for the animal showed a difference of only 21% more DMI for protein compared with fat, whereas the sire genomic regressions indicated approximately 35% more DMI for fat than protein. Estimates of annual maintenance in kilograms DMI/kilograms BW per lactation were similar from phenotypic regression (5.9 ± 0.14), genomic regression (5.8 ± 0.31), and sire genomic regression multiplied by 2 (5.3 ± 0.55) and are larger than those estimated by the National Academies for Science, Engineering, and Medicine based on NEL equations. Because of the high correspondence between the three regressions, these maintenance values should probably be preferred.

Observations and conclusions from the study: Using national genomic estimated breeding values (GEBV) enabled precise separation of genetic and environmental co-variances in feed intake analysis. Genomic regression for DMI on yield components exceeded phenotypic regression estimates. Genomic regressions, based on standardized milk (3.5% fat, 3.0% protein), indicated a 2.6-fold increase in marginal feed requirements compared to phenotypic regression for milk components. Interestingly, the feed required for milk volume seems lower than previously assumed. Genomic regressions on milk components and BW composite provide more accurate insights into feed and maintenance costs compared to phenotypic regressions. The results strongly suggest that selecting smaller sized cows with negative residual feed intake (RFI) and higher milk, fat, and protein production, as indicated by a relevant index, tends to increase profitability. National indexes should prioritize reducing body size in dairy cattle for improved feed efficiency and profitability.